Socially and contextually appropriate recommendation systems

ABSTRACT

Systems and methods may provide for conducting an interest analysis of data associated with a user, wherein the interest analysis distinguishes between abstract interests and social interests. Additionally, one or more recommendations may be generated for the user based on the interest analysis and a current context of the user, wherein the one or more recommendations may be presented to the user. In one example, the abstract interests identify types of topics and types of objects, and the social interests identify types of social groups.

TECHNICAL FIELD

Embodiments generally relate to recommendation systems. Moreparticularly, embodiments relate to socially and contextuallyappropriate recommendation systems.

BACKGROUND

Recommendation systems may be deployed in a variety of applications suchas social networking sites (e.g., FACEBOOK), search engines (e.g.,BING), video sharing sites (e.g., YOUTUBE), electronic commerce(e-commerce) sites (e.g., AMAZON.COM), and so forth, wherein content maybe recommended to users based on the users' past online activity.Conventional solutions, however, may recommend content that isappropriate for a user from one perspective but inappropriate for theuser from another perspective. For example, particular content might berecommended to a user due to the type of content (e.g., the content isassociated with a favorite genre and/or subject matter of the user),whereas that content may be undesirable to the user from a socialperspective (e.g., the content originates from a group of individualsdisliked by the user). Additionally, content may be recommended to auser due to the social relevance of the content (e.g., the contentoriginates from a favorite social group of the user), whereas the typeof content might be undesirable from the user's perspective. Moreover,conventional systems may recommend content that is appropriate for auser at one moment in time but is inappropriate at another moment intime (e.g., due to the user's social setting and/or surroundings).

BRIEF DESCRIPTION OF THE DRAWINGS

The various advantages of the embodiments will become apparent to oneskilled in the art by reading the following specification and appendedclaims, and by referencing the following drawings, in which:

FIG. 1 is a block diagram of an example of a recommendation pipelineaccording to an embodiment;

FIG. 2 is a flowchart of an example of a method of recommending contentaccording to an embodiment;

FIG. 3 is a flowchart of an example of a method of distinguishingbetween abstract interests and social interests according to anembodiment;

FIG. 4 is a flowchart of an example of a method of using personas torecommend content and control electronic file visibility according to anembodiment;

FIG. 5 is an illustration of an example of a set of personas accordingto an embodiment;

FIG. 6 is a flowchart of an example of a method of adapting personasaccording to an embodiment;

FIG. 7 is an illustration of an example of an electronic file visibilitymodification according to an embodiment;

FIG. 8 is a flowchart of an example of a method of adapting interestsaccording to an embodiment;

FIG. 9 is an illustration of an example of a plurality ofrecommendations and associated interests according to an embodiment

FIG. 10 is a block diagram of an example of a processor according to anembodiment; and

FIG. 11 is a block diagram of an example of a system according to anembodiment.

DESCRIPTION OF EMBODIMENTS

Turning now to FIG. 1, a recommendation pipeline 20 is shown. Thepipeline 20 may generally include a crawling module 22, an interestdetection module 24, a visualization module 26 (26 a, 26 b), arecommendation module 28 (28 a, 28 b), a sensor inference module 30 anda front end interface 34. The illustrated crawling module 22 collectsdata (e.g., tags, comments, “likes”, geo-location details, etc.) fromone or more of a social network, an online profile, a posted document oran authored document, wherein the collected data is associated with auser. More particularly, the crawling module 22 may collect user posts,photos, etc., along with their corresponding tags, comments, time ofday, geo-location details, and so forth, on a static (e.g., periodic) ordynamically determined time basis.

For example, the crawling module 22 might determine the number of likesreceived by the photos and other content posted online by a user and herfriends wherein, from this data the crawling module 22 may calculate theaverage sharing rate of the group as the time difference between contentposts. The average sharing rate may then be used to determine how oftento collect content from the group in question. The crawling module 22may also subscribe to content from a particular content provider (e.g.,user, web site, etc.), calculate the average sharing rate of the contentprovider, and use the average sharing rate to determine how often tocollect content from the content provider. The data collected by thecrawling module 22 may provide broad ranging insight into the user'spreferences, relationships, and/or habits.

The interest detection module 24 may generally conduct an interestanalysis of the collected data, wherein the interest analysisdistinguishes between abstract interests and social interests. Moreparticularly, the abstract interests may identify types of topics (e.g.,genres, themes, subject matters) and types of objects (e.g., documents,video, audio, calendar events), whereas the social interests mayidentify types of social groups (e.g., family members, co-workers,neighbors and other groups). As will be discussed in greater detail,distinguishing between abstract interests and social interests enablesthe illustrated pipeline 20 to identify more relevant content for theuser by, for example, potentially eliminating/downgradingrecommendations that might be appropriate from an abstract intereststandpoint but are inappropriate from a social interest standpoint (andvice versa).

The recommendation module 28 may generate one or more recommendationsfor the user based on the interest analysis and a current context of theuser, wherein the sensor inference module 30 may determine the currentcontext based on sensor data 32 (e.g., location data and/or socialproximity data) associated with the user. For example, the location datamay include Global Positioning System (GPS) coordinates, streetaddresses, etc., and the social proximity data may indicate who isnearby relative to the user. In one example, the social proximity datais determined via one or more wireless transmissions (e.g., near fieldcommunications/NFC, Wi-Fi, Bluetooth, etc.) between the user's deviceand the devices of nearby individuals and/or networks. Moreover, thefront end interface 34 may present, among other things, the one or morerecommendations to the user. As will be discussed in greater detail,using the sensor data 32 to generate the recommendations may enable thepipeline 20 to identify more relevant content for the user by, forexample, potentially eliminating/downgrading recommendations that areinappropriate at a given moment in time but might be appropriate atanother moment in time (e.g., in different social settings and/orsurroundings).

Thus, FIG. 2 demonstrates a method 54 of recommending content. Themethod 54 may be implemented as one or more modules in a set of logicinstructions stored in a machine- or computer-readable storage mediumsuch as random access memory (RAM), read only memory (ROM), programmableROM (PROM), firmware, flash memory, etc., in configurable logic such as,for example, programmable logic arrays (PLAs), field programmable gatearrays (FPGAs), complex programmable logic devices (CPLDs), infixed-functionality hardware logic using circuit technology such as, forexample, application specific integrated circuit (ASIC), complementarymetal oxide semiconductor (CMOS) or transistor-transistor logic (TTL)technology, or any combination thereof. For example, computer programcode to carry out operations shown in method 54 may be written in anycombination of one or more programming languages, including an objectoriented programming language such as JAVA, Smalltalk, C++ or the likeand conventional procedural programming languages, such as the “C”programming language or similar programming languages.

Illustrated processing block 56 provides for collecting data associatedwith a user from one or more social networks, wherein an interestanalysis of the data may be conducted at block 58. As already noted, theinterest analysis may distinguish between abstract interests and socialinterests. Block 60 may determine the current context of the user basedon location data and/or social proximity data associated with the user.Additionally, one or more recommendations may be generated at block 62based on the interest analysis and the current context of the user.Illustrated block 64 presents the one or more recommendations to theuser.

Abstract Interests and Social Interests

FIG. 3 shows a method 36 of distinguishing between abstract interestsand social interests. The method 36 may be readily substituted for block58 (FIG. 2), already discussed. Moreover, the method 36 may beimplemented as one or more modules such as, for example, the interestdetection module 24 (FIG. 1), in a set of logic instructions stored in amachine- or computer-readable storage medium such as RAM, ROM, PROM,firmware, flash memory, etc., in configurable logic such as, forexample, PLAs, FPGAs, CPLDs, in fixed-functionality hardware logic usingcircuit technology such as, for example, ASIC, CMOS or TTL technology,or any combination thereof.

The illustrated method 36 generally acknowledges that a user's abstractinterests may be driven by the user's passion for particular themes,whereas the user's social interests may be driven by the user'sattraction and social ties to particular groups of individuals.Accordingly, illustrated processing block 40 uses the data 38 from theuser's social networks, online profiles, posted documents and/orauthored documents (e.g., collectively, the user's “social content”) todetect one or more topics. These different topics may be considered tobe potential abstract interests.

In one example, a clustering based topic modeling algorithm such as, forexample, Latent Dirichlet Allocation (LDA), is used to obtain the topicspresent in the user's social content. LDA may be fed a series ofdocuments, and from these documents discover the “topics” that occur inthe collection of documents. A “topic” may be a collection of words thatoccur frequently together. Each document that is fed to LDA may eitherbe a post from the user's social network (e.g., a photo with its tags,text from their online profile) or an online document owned by the user.The affinity that each post has to each discovered topic may then beobtained, and labeled with its most affined topic. Comments and likesfrom social media data may be labeled with the same topic of theirparent post.

The amount of content a user has generated towards each specific topicmay then be measured. In one example, the “K” topics with the mostgenerated content may be defined at block 42 as the user's abstractinterests. To leverage interests that may be more socially driven (e.g.,the user's friends are discussing topic “X”, so the user also postsabout topic X), the designer of the interest detection module may bepermitted to set how much certain user actions and certain socialcontexts are considered for weighting the user's abstract interests.Some designers might consider that if the user never posts about topicX, but rather only likes things about topic X that a particular socialgroup or person posts, then topic X is not part of the user's abstractinterests (e.g., instead topic X is a more socially driven interest).Similarly, other designers might not want to penalize the detection oftopic X as an abstract interest when the user is merely liking orcommenting on the content a particular person posts about X. In eitherinstance, distinguishing between abstract interests and social interestsmay enable the recommendation system to be more effectively tailored tofit particular applications and/or circumstances.

In general, block 44 may provide for using the data 38 from the user'ssocial content to detect one or more social groups. Illustrated block 46discovers and characterizes the social groups present in the user'ssocial network, wherein these social groups may be defined at block 48as the user's social interests. In one example, social groups arecharacterized based on their social tie to the user, wherein a socialgroup may be considered as including a set of users that tend tointeract on the same pieces of content.

More particularly, the user's social content may be divided into timeperiods, and for each time period “t” a connection may be createdbetween two users when under the same time period they commented orliked the same piece of content. Such an approach may result in thecreation of a series of graphs. A technique such as, for example, theClique Percolation Method (CPM) may be used to identify unique sets ofcliques or groups Gi(t). Each of these groups may have users whointeracted at least once on the same content in the studied time period.The interest detection module may also consider that social groups existfor some minimum period of time. Therefore, for each of the discoveredgroups in time period t, a determination may be made as to whether atleast y % of its members are also present in the group for time periodt+1. A set of groups that for J periods of time have been continuouslypresent in the user's social network may then be identified; thesepersistent, non-ephemeral groups, may be labeled at block 44 as thesocial groups of the user's social network. These social groups may alsorepresent the possible social interests of the user.

Block 46 may characterize the social tie the user has to each of thesocial groups detected in block 44. Example variables that may beanalyzed to measure a user's social tie to others, include, but are notlimited to:

Intensity: this variable may measure the amount of attention a usergives to a particular social group, based on the amount of onlineinteractions directed to the group.

Intimacy: this variable may measure how close the user is to aparticular social group, based on amount of intimacy words used in theirsocial interactions. In one example, a linguistic dictionary such as,for example, a Linguistic Inquiry and Word Count (LIWC) dictionary, isused to determine the words that denote intimacy. A linguisticdictionary may help identify which words in a text match lists ofcategorical word stems. Intimacy words may be considered to be wordsthat match categories related to, for example: Family, Friends, Home,Sexual, Swears, Work, Leisure, Money, Body, Religion, Health, and soforth.

Social connections: this variable may measure how many socialconnections in common a user has with a particular social group, basedon the number of mutual friends, number of same online groups.

Emotional Variables: this variable may measure how emotional a user iswhen interacting with a particular social group based on the amount ofarousal words used in the interaction. Arousal words may be identifiedby again matching words in user generated content against a linguisticdictionary that has an arousal category.

For each of the group variables, their values may be normalized withrespect to the user's typical behavior with the rest of their socialnetwork. These variables may represent the social tie vector of thegroup. A mean-shift algorithm may be used to cluster together groupsthat present similar social tie vectors, wherein each of these clustersof groups may be considered to represent the different types of socialgroups present in the user's network. Thus, each cluster may be definedat block 48 as one of the user's social interests.

Block 50 may learn the user's interest preferences based on the definedabstract interests and social interests, and generate weighted interestswith relevant metadata 52. More particularly, the preferences that theuser manifests towards a possible social or abstract interest may beupdated in block 50 based on the user's actions. Initially, it may beconsidered that all possible interests have the same weight. Goingforward, different user interactions may influence the weight valuesdifferently. For example, if the user mentions or comments on contentfrom a member of a social group belonging to a particular socialinterest, that interaction may increase the weight value between theuser and that social interest. Likewise, if the user posts or commentson content related to an abstract interest, that interaction mayincrease the weight value between the user and the abstract interest.Accordingly, as time passes, illustrated block 50 learns the user'spreferences.

If after a period of time the user does not provide interactions to thesystem, the system may recommend abstract and social interests that arethe most popular in the user's community. Such an approach may functionto overcome “cold starts”.

The updating of relationship strengths may occur after each userinteraction. The strength of relationship between a user “U” and aninterest “I” (abstract or social) may be calculated as an example via asimple formula for simulated annealing (e.g., reinforcement learning).W _(I) ^(U)(I)_(t) =α*W _(I) ^(U)(I)_(t-1)+(1−α)*A  (1)

Where, W_(I) ^(U)(I)_(t) is the new weight that the user U hasmanifested for interest I, W_(I) ^(U)(I)_(t-1) is the previous weightthe user had manifested for the interest at time t−1, α is the learningrate of the system, and A is the action that user expresses to theinterest I. The value of A may be set by the system designer. Forexample, the system designer may give different weights to mentioning aparticular person in content, sharing particular content, commenting oncertain content, liking certain content, among other possible userinteractions. The interest detection module may then send the list ofdiscovered interests, each with their categorization (abstract orsocial), weight (user preference) and characterization, to avisualization module such as the visualization module 26 (FIG. 1).

Personas

With continuing reference to FIGS. 1 and 4, a method 66 of usingpersonas to recommend content and control electronic file visibility isshown. The method 66 may be implemented as one or more modules such as,for example, the visualization module 26 and/or the recommendationmodule 28 of the pipeline 20, in a set of logic instructions stored in amachine- or computer-readable storage medium such as RAM, ROM, PROM,firmware, flash memory, etc., in configurable logic such as, forexample, PLAs, FPGAs, CPLDs, in fixed-functionality hardware logic usingcircuit technology such as, for example, ASIC, CMOS or TTL technology,or any combination thereof.

Illustrated block 68 provides for creating one or more personas for theuser based on the interest analysis, wherein an active persona may beautomatically selected at block 70 based on the current context of theuser. One or more recommendations may be generated at block 72 based onthe active persona. Additionally, illustrated block 74 modifies thevisibility of one or more electronic files based on the active persona.

In one example, the visualization module 26 includes a persona creator26 b that creates the personas based on the interest analysis and therecommendation module 28 includes a persona selector 28 b thatautomatically selects the active persona based on the current context ofthe user. The persona selector 28 b may use a decision tree toautomatically identify the most appropriate user persona to be enabled.More particularly, the persona selector 28 b may first analyze whetherthe current location of the user is mapped to a particular persona. Ifmore than one persona is associated with that location, or if no personahas that location, the persona selector 28 b may analyze whether theindividuals around the user are mapped to a persona. Based on thisinformation, the persona selector 28 b may enable the most appropriatepersona. If the location and individuals around the user have nomapping, the persona selector 28 b may consider by default all of thediscovered interests of the user.

The persona selector 28 b may also enable the user to manually selecttheir most appropriate persona via a menu, with the most probablepersonas being displayed on top for ease of selection. For example, thevisualization module 26 may present, via the front end interface 34, thepersonas to the user along with the underlying basis for the personas,wherein the front end interface 34 may receive user input regarding thepersonas. The persona creator 26 b may adapt the personas based on theuser input. Such an approach may provide greater transparency of thesystem to the user and may improve overall performance. Thus, therecommendation module 28 may provide social content recommendations thatare the most interesting to a user, and that are attuned with thecurrent persona the user seeks to portray.

FIG. 5 demonstrates that a set of personas 76 (76 a-76 c) may be createdfor a user. In the illustrated example, a work persona 76 a isactivated/enabled when the user is at work or around her co-workers,wherein the work persona 76 may be created as a result of the socialinterests of the boss's family 78 and the abstract interests of computertechnology 80 and cars 82. On the other hand, a neighborhood persona 76b might be activated/enabled when the user is at home, within fiftymeters from home or around her neighbors, wherein the neighborhoodpersona 76 b may be created from the social interests of the user'sfamily 84 and the abstract interests of neighborhood repair 86 andanimal rights 88. In addition, a game night persona 76 c may beactivated/enabled when the user is at a particular game center (e.g.,bingo hall), wherein the game night persona 76 c may beactivated/enabled based on the social interests of Bobby and friends 90,and the abstract interests of poker tips 92, board game discussions 94,and aggressive discussions from Mark 96.

As already noted, the personas 76 as well as the underlying basis forthe personas 76 may be made visible to the user. In this regard, variousvisualization techniques may be used to convey the types of interests(e.g., abstract versus social) as well as the relative weights of theunderlying interests. For example, the illustrated approach varies theline consistency of the bubble/circle enclosing each interest based onthe type of interest. Thus, the user may readily determine that cars 82is an abstract interest (e.g., due to the solid line) for the workpersona 76 a, the user's family 84 is a social interest (e.g., due tothe dashed line) for the neighbor persona 76 b, board game discussions94 is an abstract interest for the game night persona 76 c, and soforth. Moreover, the illustrated approach varies the size of thebubble/circle enclosing each interest based on the weight of theinterest. Thus, the user may readily determine that the social interestof computer technology 80 has very strong tie to the work persona 76 a,the abstract interest of neighborhood repair 86 has a moderate tie tothe neighbor persona 76 b, the abstract interest of poker tips 92 has aweak tie to the game night persona 76 c, and so forth. Othervisualization techniques such as, for example, different colors, icons,etc., may also be used.

FIG. 6 shows a method 98 of adapting personas. The method 98 may beimplemented as one or more modules such as, for example, the personacreator 26 b (FIG. 1), in a set of logic instructions stored in amachine- or computer-readable storage medium such as RAM, ROM, PROM,firmware, flash memory, etc., in configurable logic such as, forexample, PLAs, FPGAs, CPLDs, in fixed-functionality hardware logic usingcircuit technology such as, for example, ASIC, CMOS or TTL technology,or any combination thereof.

Illustrated block 100 provides for presenting one or more personas suchas, for example, the set of personas 76 (FIG. 5), to a user along withthe underlying basis for the personas, wherein user input regarding thepersonas may be received at block 102. The user input may include, forexample, changes to the personas (e.g., persona titles), the currentcontext parameters used to activate the personas (e.g., where activatedand/or around whom), the associated interests (e.g., social and/orabstract), and so forth. For instance, the user might state that“whenever I am at work or near one of my co-workers, enable my WorkPersona, who is interested in Augmented Reality, Crafts, Stock Market,and positive online interactions involving may manager and work buddies;when I am with my family or friends, enable my Fun Aunt Persona, who isinterested in Hunting, Crafts, STEM Education, and any onlineinteractions from my family.” Such an approach may enable the user tohave more control over the type of content that they will receive andthe type of personal interests their devices showcase under a certainscenario. Block 104 may adapt the one or more personas based on the userinput.

File Visibility

With continuing reference to FIGS. 1 and 7, a scenario is demonstratedin which personas are used to modify the visibility of one or moreelectronic files stored on a device (e.g., desktop computer, notebookcomputer, tablet computer, convertible tablet, smart phone, personaldigital assistant/PDA, mobile Internet device/MID) associated with theuser. In the illustrated example, when the neighbor persona 76 b isactive, a directory listing 106 shows a folder entitled “Job search”, adocument entitled “Client lists” and a media file entitled “Vacationvideo”. When the work persona 76 a is active, on the other hand, the“Job search” folder and the “Vacation video” media file are hidden fromview. Accordingly, the co-workers, managers, etc., of the user may beautomatically prevented from seeing particular files, in the illustratedexample. The files that are selectively hidden may include, but are notlimited to, computer programs (e.g., executable files), documents,spreadsheets, databases, social networking content (e.g., photos, friendlistings and other profile information), folders, and so forth. Theillustrated approach therefore provides the user with a greater level ofself-presentation that is sensitive to different social settings and/orsurroundings.

In one example, the recommendation module 28 of the pipeline 20 includesan operating system interface 28 a to modify the visibility of one ormore electronic files based on the active persona.

Interest Modifications

With continuing reference to FIGS. 1 and 8, a method 108 of adaptinginterests is shown. The method 108 may be implemented as one or moremodules such as, for example, the visualization module 26 of thepipeline 20, in a set of logic instructions stored in a machine- orcomputer-readable storage medium such as RAM, ROM, PROM, firmware, flashmemory, etc., in configurable logic such as, for example, PLAs, FPGAs,CPLDs, in fixed-functionality hardware logic using circuit technologysuch as, for example, ASIC, CMOS or TTL technology, or any combinationthereof.

Illustrated block 110 provides for presenting the underlying basis forone or more recommendations to a user, wherein user input regarding therecommendations may be received at block 112. Additionally, the abstractinterests and/or social interests of the user may be adapted at block114 based on the user input.

In one example, the visualization module 26 presents, via the front endinterface 34, the underlying basis for the recommendations, wherein thefront end interface 34 receives the user input regarding therecommendations. The visualization module 26 may also include aninterest modifier 26 a that adapts one or more of the abstract interestsor the social interests based on the user input.

FIG. 9 demonstrates that a set of recommended content 116 (116 a-116 e,e.g., one or more recommendations) may be presented to the user alongwith the underlying basis for the recommended content 116. In theillustrated example, when the user hovers the cursor over a particularrecommendation such as, for example, a photo recommendation 116 b(“Photo B”), the user is provided with an underlying basis explanation118 (e.g., “Photo tags are noodle, delicious, meal, and you seem to beinto: food). Similarly, when the user hovers the cursor over aparticular interest such as, for example, an abstract interest 120, theuser may be provided with an underlying basis explanation 122 (e.g.,“still life, white, green, lime, lemonade, mint, mojito”). The specificexamples provided herein are to facilitate discussion only and may varydepending upon the circumstances.

FIG. 10 illustrates a processor core 200 according to one embodiment.The processor core 200 may be the core for any type of processor, suchas a micro-processor, an embedded processor, a digital signal processor(DSP), a network processor, or other device to execute code. Althoughonly one processor core 200 is illustrated in FIG. 10, a processingelement may alternatively include more than one of the processor core200 illustrated in FIG. 10. The processor core 200 may be asingle-threaded core or, for at least one embodiment, the processor core200 may be multithreaded in that it may include more than one hardwarethread context (or “logical processor”) per core.

FIG. 10 also illustrates a memory 270 coupled to the processor core 200.The memory 270 may be any of a wide variety of memories (includingvarious layers of memory hierarchy) as are known or otherwise availableto those of skill in the art. The memory 270 may include one or morecode 213 instruction(s) to be executed by the processor core 200,wherein the code 213 may implement the method 54 (FIG. 2), the method 66(FIG. 4), the method 98 (FIG. 6) and/or the method 108 (FIG. 8), alreadydiscussed. The processor core 200 follows a program sequence ofinstructions indicated by the code 213. Each instruction may enter afront end portion 210 and be processed by one or more decoders 220. Thedecoder 220 may generate as its output a micro operation such as a fixedwidth micro operation in a predefined format, or may generate otherinstructions, microinstructions, or control signals which reflect theoriginal code instruction. The illustrated front end portion 210 alsoincludes register renaming logic 225 and scheduling logic 230, whichgenerally allocate resources and queue the operation corresponding tothe convert instruction for execution.

The processor core 200 is shown including execution logic 250 having aset of execution units 255-1 through 255-N. Some embodiments may includea number of execution units dedicated to specific functions or sets offunctions. Other embodiments may include only one execution unit or oneexecution unit that can perform a particular function. The illustratedexecution logic 250 performs the operations specified by codeinstructions.

After completion of execution of the operations specified by the codeinstructions, back end logic 260 retires the instructions of the code213. In one embodiment, the processor core 200 allows out of orderexecution but requires in order retirement of instructions. Retirementlogic 265 may take a variety of forms as known to those of skill in theart (e.g., re-order buffers or the like). In this manner, the processorcore 200 is transformed during execution of the code 213, at least interms of the output generated by the decoder, the hardware registers andtables utilized by the register renaming logic 225, and any registers(not shown) modified by the execution logic 250.

Although not illustrated in FIG. 10, a processing element may includeother elements on chip with the processor core 200. For example, aprocessing element may include memory control logic along with theprocessor core 200. The processing element may include I/O control logicand/or may include I/O control logic integrated with memory controllogic. The processing element may also include one or more caches.

Referring now to FIG. 11, shown is a block diagram of a system 1000embodiment in accordance with an embodiment. Shown in FIG. 11 is amultiprocessor system 1000 that includes a first processing element 1070and a second processing element 1080. While two processing elements 1070and 1080 are shown, it is to be understood that an embodiment of thesystem 1000 may also include only one such processing element.

The system 1000 is illustrated as a point-to-point interconnect system,wherein the first processing element 1070 and the second processingelement 1080 are coupled via a point-to-point interconnect 1050. Itshould be understood that any or all of the interconnects illustrated inFIG. 11 may be implemented as a multi-drop bus rather thanpoint-to-point interconnect.

As shown in FIG. 11, each of processing elements 1070 and 1080 may bemulticore processors, including first and second processor cores (i.e.,processor cores 1074 a and 1074 b and processor cores 1084 a and 1084b). Such cores 1074 a, 1074 b, 1084 a, 1084 b may be configured toexecute instruction code in a manner similar to that discussed above inconnection with FIG. 10.

Each processing element 1070, 1080 may include at least one shared cache1896 a, 1896 b. The shared cache 1896 a, 1896 b may store data (e.g.,instructions) that are utilized by one or more components of theprocessor, such as the cores 1074 a, 1074 b and 1084 a, 1084 b,respectively. For example, the shared cache 1896 a, 1896 b may locallycache data stored in a memory 1032, 1034 for faster access by componentsof the processor. In one or more embodiments, the shared cache 1896 a,1896 b may include one or more mid-level caches, such as level 2 (L2),level 3 (L3), level 4 (L4), or other levels of cache, a last level cache(LLC), and/or combinations thereof.

While shown with only two processing elements 1070, 1080, it is to beunderstood that the scope of the embodiments are not so limited. Inother embodiments, one or more additional processing elements may bepresent in a given processor. Alternatively, one or more of processingelements 1070, 1080 may be an element other than a processor, such as anaccelerator or a field programmable gate array. For example, additionalprocessing element(s) may include additional processors(s) that are thesame as a first processor 1070, additional processor(s) that areheterogeneous or asymmetric to processor a first processor 1070,accelerators (such as, e.g., graphics accelerators or digital signalprocessing (DSP) units), field programmable gate arrays, or any otherprocessing element. There can be a variety of differences between theprocessing elements 1070, 1080 in terms of a spectrum of metrics ofmerit including architectural, micro architectural, thermal, powerconsumption characteristics, and the like. These differences mayeffectively manifest themselves as asymmetry and heterogeneity amongstthe processing elements 1070, 1080. For at least one embodiment, thevarious processing elements 1070, 1080 may reside in the same diepackage.

The first processing element 1070 may further include memory controllerlogic (MC) 1072 and point-to-point (P-P) interfaces 1076 and 1078.Similarly, the second processing element 1080 may include a MC 1082 andP-P interfaces 1086 and 1088. As shown in FIG. 11, MC's 1072 and 1082couple the processors to respective memories, namely a memory 1032 and amemory 1034, which may be portions of main memory locally attached tothe respective processors. While the MC 1072 and 1082 is illustrated asintegrated into the processing elements 1070, 1080, for alternativeembodiments the MC logic may be discrete logic outside the processingelements 1070, 1080 rather than integrated therein.

The first processing element 1070 and the second processing element 1080may be coupled to an I/O subsystem 1090 via P-P interconnects 1076 1086,respectively. As shown in FIG. 11, the I/O subsystem 1090 includes P-Pinterfaces 1094 and 1098. Furthermore, I/O subsystem 1090 includes aninterface 1092 to couple I/O subsystem 1090 with a high performancegraphics engine 1038. In one embodiment, bus 1049 may be used to couplethe graphics engine 1038 to the I/O subsystem 1090. Alternately, apoint-to-point interconnect may couple these components.

In turn, I/O subsystem 1090 may be coupled to a first bus 1016 via aninterface 1096. In one embodiment, the first bus 1016 may be aPeripheral Component Interconnect (PCI) bus, or a bus such as a PCIExpress bus or another third generation I/O interconnect bus, althoughthe scope of the embodiments are not so limited.

As shown in FIG. 11, various I/O devices 1014 (e.g., cameras, sensors)may be coupled to the first bus 1016, along with a bus bridge 1018 whichmay couple the first bus 1016 to a second bus 1020. In one embodiment,the second bus 1020 may be a low pin count (LPC) bus. Various devicesmay be coupled to the second bus 1020 including, for example, akeyboard/mouse 1012, network controllers/communication device(s) 1026(which may in turn be in communication with a computer network), and adata storage unit 1019 such as a disk drive or other mass storage devicewhich may include code 1030, in one embodiment. The code 1030 mayinclude instructions for performing embodiments of one or more of themethods described above. Thus, the illustrated code 1030 may implementthe method 54 (FIG. 2), the method 66 (FIG. 4), the method 98 (FIG. 6)and/or the method 108 (FIG. 8), already discussed, and may be similar tothe code 213 (FIG. 10), already discussed. Further, an audio I/O 1024may be coupled to second bus 1020.

Note that other embodiments are contemplated. For example, instead ofthe point-to-point architecture of FIG. 11, a system may implement amulti-drop bus or another such communication topology. Also, theelements of FIG. 11 may alternatively be partitioned using more or fewerintegrated chips than shown in FIG. 11.

ADDITIONAL NOTES AND EXAMPLES

Example 1 may include an apparatus to recommend content, comprising aninterest detection module to conduct an interest analysis of dataassociated with a user, wherein the interest analysis distinguishesbetween abstract interests and social interests, a recommendation moduleto generate one or more recommendations for the user based on theinterest analysis and a current context of the user, and a visualizationmodule to present the one or more recommendations to the user.

Example 2 may include the apparatus of Example 1, wherein the abstractinterests are to identify types of topics and types of objects, andwherein the social interests are to identify types of social groups.

Example 3 may include the apparatus of Example 1, further including apersona creator to create one or more personas for the user based on theinterest analysis, and a persona selector to select an active personabased on the current context of the user, wherein the one or morerecommendations are to be generated based on the active persona.

Example 4 may include the apparatus of Example 3, further including avisualization module to present, via the front end interface, the one ormore personas to the user along with an underlying basis for the one ormore personas, wherein the front end interface is to receive user inputregarding the one or more personas, and the persona creator is to adaptthe one or more personas based on the user input.

Example 5 may include the apparatus of Example 3, further including anoperating system interface to modify a visibility of one or moreelectronic files based on the active persona.

Example 6 may include the apparatus of Example 1, further including avisualization module to present, via the front end interface, anunderlying basis for the one or more recommendations to the user,wherein the front end interface is to receive user input regarding theone or more recommendations, and an interest modifier to adapt one ormore of the abstract interests or the social interests based on the userinput.

Example 7 may include the apparatus of any one of Examples 1 to 6,further including a crawling module to collect the data associated withthe user from one or more of a social network, an online profile, aposted document or an authored document.

Example 8 may include the apparatus of any one of Examples 1 to 6,further including a sensor inference module to determine the currentcontext based on one or more of location data or social proximity dataassociated with the user.

Example 9 may include a method of recommending content, comprisingconducting an interest analysis of data associated with a user, whereinthe interest analysis distinguishes between abstract interests andsocial interests, generating one or more recommendations for the userbased on the interest analysis and a current context of the user, andpresenting the one or more recommendations to the user.

Example 10 may include the method Example 9, wherein the abstractinterests identify types of topics and types of objects, and wherein thesocial interests identify types of social groups.

Example 11 may include the method of Example 9, further includingcreating one or more personas for the user based on the interestanalysis, and selecting an active persona based on the current contextof the user, wherein the one or more recommendations are generated basedon the active persona.

Example 12 may include the method of Example 11, further includingpresenting the one or more personas to the user along with an underlyingbasis for the one or more personas, receiving user input regarding theone or more personas, and adapting the one or more personas based on theuser input.

Example 13 may include the method of Example 11, further includingmodifying a visibility of one or more electronic files based on theactive persona.

Example 14 may include the method of Example 9, further includingpresenting an underlying basis for the one or more recommendations tothe user, receiving user input regarding the one or morerecommendations, and adapting one or more of the abstract interests orthe social interests based on the user input.

Example 15 may include the method of any one of Examples 9 to 14,further including collecting the data associated with the user from oneor more of a social network, an online profile, a posted document or anauthored document.

Example 16 may include the method of any one of Examples 9 to 14,further including determining the current context based on one or moreof location data or social proximity data associated with the user.

Example 17 may include at least one computer readable storage mediumcomprising a set of instructions which, when executed by a computingdevice, cause the computing device to conduct an interest analysis ofdata associated with a user, wherein the interest analysis is todistinguish between abstract interests and social interests, and presentthe one or more recommendations to the user.

Example 18 may include the at least one computer readable storage mediumof Example 17, wherein the abstract interests are to identify types oftopics and types of objects, and wherein the social interests are toidentify types of social groups.

Example 19 may include the at least one computer readable storage mediumof Example 17, wherein the instructions, when executed, cause acomputing device to create one or more personas for the user based onthe interest analysis, and select an active persona based on the currentcontext of the user, wherein the one or more recommendations are to begenerated based on the active persona.

Example 20 may include the at least one computer readable storage mediumof Example 19, wherein the instructions, when executed, cause acomputing device to present the one or more personas to the user alongwith an underlying basis for the one or more personas, receive userinput regarding the one or more personas, and adapt the one or morepersonas based on the user input.

Example 21 may include the at least one computer readable storage mediumof Example 19, wherein the instructions, when executed, cause acomputing device to modify a visibility of one or more electronic filesbased on the active persona.

Example 22 may include the at least one computer readable storage mediumof Example 17, wherein the instructions, when executed, cause acomputing device to present an underlying basis for the one or morerecommendations to the user, receive user input regarding the one ormore recommendations, and adapt one or more of the abstract interests orthe social interests based on the user input.

Example 23 may include the at least one computer readable storage mediumof any one of Examples 17 to 22, wherein the instructions, whenexecuted, cause a computing device to collect the data associated withthe user from one or more of a social network, an online profile, aposted document or an authored document.

Example 24 may include the at least one computer readable storage mediumof any one of Examples 17 to 22, wherein the instructions, whenexecuted, cause a computing device to determine the current contextbased on one or more of location data or social proximity dataassociated with the user.

Example 25 may include an apparatus to recommend content, comprisingmeans for performing the method of any one of Examples 9 to 16.

Thus, techniques described herein may consider how the co-participationbehavior of content users might also influence a particular userinterest for such content. Moreover, all of the different socialdynamics that might lead a user to be interested in certain content maybe considered. For example, some users may be more driven to participatein content where they know the other individuals participating and theconversations are very emotional. Leveraging such information in therecommendation process may yield much more accurate and effectiveresults. Additionally, social content recommendations may be attuned tothe type of persona that the user currently wishes to portray.Techniques may also provide transparent data representations that allowthe user to understand why the system classified certain content as anabstract or social interest, and the data elements that are influencingeach interest. In addition, techniques enable the user to easilyunderstand the identified interests from their data, as well as defineand control the type of recommendations received for each of theirportrayed personas.

Embodiments are applicable for use with all types of semiconductorintegrated circuit (“IC”) chips. Examples of these IC chips include butare not limited to processors, controllers, chipset components,programmable logic arrays (PLAs), memory chips, network chips, systemson chip (SoCs), SSD/NAND controller ASICs, and the like. In addition, insome of the drawings, signal conductor lines are represented with lines.Some may be different, to indicate more constituent signal paths, have anumber label, to indicate a number of constituent signal paths, and/orhave arrows at one or more ends, to indicate primary information flowdirection. This, however, should not be construed in a limiting manner.Rather, such added detail may be used in connection with one or moreexemplary embodiments to facilitate easier understanding of a circuit.Any represented signal lines, whether or not having additionalinformation, may actually comprise one or more signals that may travelin multiple directions and may be implemented with any suitable type ofsignal scheme, e.g., digital or analog lines implemented withdifferential pairs, optical fiber lines, and/or single-ended lines.

Example sizes/models/values/ranges may have been given, althoughembodiments are not limited to the same. As manufacturing techniques(e.g., photolithography) mature over time, it is expected that devicesof smaller size could be manufactured. In addition, well knownpower/ground connections to IC chips and other components may or may notbe shown within the figures, for simplicity of illustration anddiscussion, and so as not to obscure certain aspects of the embodiments.Further, arrangements may be shown in block diagram form in order toavoid obscuring embodiments, and also in view of the fact that specificswith respect to implementation of such block diagram arrangements arehighly dependent upon the platform within which the embodiment is to beimplemented, i.e., such specifics should be well within purview of oneskilled in the art. Where specific details (e.g., circuits) are setforth in order to describe example embodiments, it should be apparent toone skilled in the art that embodiments can be practiced without, orwith variation of, these specific details. The description is thus to beregarded as illustrative instead of limiting.

The term “coupled” may be used herein to refer to any type ofrelationship, direct or indirect, between the components in question,and may apply to electrical, mechanical, fluid, optical,electromagnetic, electromechanical or other connections. In addition,the terms “first”, “second”, etc. may be used herein only to facilitatediscussion, and carry no particular temporal or chronologicalsignificance unless otherwise indicated.

As used in this application and in the claims, a list of items joined bythe term “one or more of” may mean any combination of the listed terms.For example, the phrases “one or more of A, B or C” may mean A; B; C; Aand B; A and C; B and C; or A, B and C.

Those skilled in the art will appreciate from the foregoing descriptionthat the broad techniques of the embodiments can be implemented in avariety of forms. Therefore, while the embodiments have been describedin connection with particular examples thereof, the true scope of theembodiments should not be so limited since other modifications willbecome apparent to the skilled practitioner upon a study of thedrawings, specification, and following claims.

We claim:
 1. An apparatus comprising: an interest detection module,implemented at least partly in one or more of configurable logic devicesor fixed functionality logic hardware, to conduct an interest analysisof data associated with a user, wherein the interest analysisdistinguishes between abstract interests and social interests byclustering data associated with the user; a recommendation module,implemented at least partly in one or more of configurable logic devicesor fixed functionality logic hardware, to generate one or morerecommendations for the user based on the interest analysis and acurrent context of the user; a front end interface to present the one ormore recommendations to the user; a persona creator to create one ormore personas for the user based on the interest analysis; a personaselector to select an active persona based on the current context of theuser, wherein the one or more recommendations are to be generated basedon the active persona; and an operating system interface to modify avisibility of one or more electronic files based on the active persona,wherein the abstract interests are to identify types of topics and typesof objects, and wherein the social interests are to identify types ofsocial groups.
 2. The apparatus of claim 1, further including avisualization module, implemented at least partly in one or more ofconfigurable logic devices or fixed functionality logic hardware, topresent, via the front end interface, the one or more personas to theuser along with an underlying basis for the one or more personas,wherein the front end interface is to receive user input regarding theone or more personas, and the persona creator is to adapt the one ormore personas based on the user input.
 3. The apparatus of claim 1,further including: a visualization module, implemented at least partlyin one or more of configurable logic devices or fixed functionalitylogic hardware, to present, via the front end interface, an underlyingbasis for the one or more recommendations to the user, wherein the frontend interface is to receive user input regarding the one or morerecommendations; and an interest modifier to adapt one or more of theabstract interests or the social interests based on the user input. 4.The apparatus of claim 1, further including a crawling module,implemented at least partly in one or more of configurable logic devicesor fixed functionality logic hardware, to collect the data associatedwith the user from one or more of a social network, an online profile, aposted document or an authored document.
 5. The apparatus of claim 1,further including a sensor inference module, implemented at least partlyin one or more of configurable logic devices or fixed functionalitylogic hardware, to determine the current context based on one or more oflocation data or social proximity data associated with the user.
 6. Amethod comprising: conducting an interest analysis of data associatedwith a user, wherein the interest analysis distinguishes betweenabstract interests and social interests by clustering data associatedwith the user; generating one or more recommendations for the user basedon the interest analysis and a current context of the user; presentingthe one or more recommendations to the user; creating one or morepersonas for the user based on the interest analysis; selecting anactive persona based on the current context of the user, wherein the oneor more recommendations are generated based on the active persona; andmodifying a visibility of one or more electronic files based on theactive persona, wherein the abstract interests identify types of topicsand types of objects, and wherein the social interests identify types ofsocial groups.
 7. The method of claim 6, further including: presentingthe one or more personas to the user along with an underlying basis forthe one or more personas; receiving user input regarding the one or morepersonas; and adapting the one or more personas based on the user input.8. The method of claim 6, further including: presenting an underlyingbasis for the one or more recommendations to the user; receiving userinput regarding the one or more recommendations; and adapting one ormore of the abstract interests or the social interests based on the userinput.
 9. The method of claim 6, further including collecting the dataassociated with the user from one or more of a social network, an onlineprofile, a posted document or an authored document.
 10. The method ofclaim 6, further including determining the current context based on oneor more of location data or social proximity data associated with theuser.
 11. At least one non-transitory computer readable storage mediumcomprising a set of instructions which, when executed by a computingdevice, cause the computing device to: conduct an interest analysis ofdata associated with a user, wherein the interest analysis is todistinguish between abstract interests and social interests byclustering data associated with the user; generate one or morerecommendations for the user based on the interest analysis and acurrent context of the user; present the one or more recommendations tothe user; create one or more personas for the user based on the interestanalysis; select an active persona based on the current context of theuser, wherein the one or more recommendations are to be generated basedon the active persona; and modify a visibility of one or more electronicfiles based on the active persona, wherein the abstract interests are toidentify types of topics and types of objects, and wherein the socialinterests are to identify types of social groups.
 12. The at least onenon-transitory computer readable storage medium of claim 11, wherein theinstructions, when executed, cause a computing device to: present theone or more personas to the user along with an underlying basis for theone or more personas; receive user input regarding the one or morepersonas; and adapt the one or more personas based on the user input.13. The at least one non-transitory computer readable storage medium ofclaim 11, wherein the instructions, when executed, cause a computingdevice to: present an underlying basis for the one or morerecommendations to the user; receive user input regarding the one ormore recommendations; and adapt one or more of the abstract interests orthe social interests based on the user input.
 14. The at least onenon-transitory computer readable storage medium of claim 11, wherein theinstructions, when executed, cause a computing device to collect thedata associated with the user from one or more of a social network, anonline profile, a posted document or an authored document.
 15. The atleast one non-transitory computer readable storage medium of claim 11,wherein the instructions, when executed, cause a computing device todetermine the current context based on one or more of location data orsocial proximity data associated with the user.